Journal of affective disorders最新文献

Background: Postpartum depression (PPD) is one of the most common mental disorders in parents after birth. To develop tailored preventive programs, it is necessary to identify risk factors for PPD in parents. This study aimed to examine the impact of parental childhood maltreatment (CM) as a risk factor for PPD.

Methods: Data from a German study comprising n = 349 mothers and n = 46 fathers were used. Hierarchical regression models were performed to examine CM, educational background, single parenthood, emotion regulation and attachment style as predictors of symptoms of PPD. In exploratory analyses, potential mediators (i.e., parenting stress and emotion regulation) were investigated via a path model.

Results: CM, low level of education, difficulties in emotion regulation, and attachment anxiety were significant predictors for maternal PPD [R² = 0.52, F (6, 305) = 57.99, p < .001]. For fathers, difficulties in emotion regulation were identified as a predictor [R² = 0.43, F (6, 24) = 4.78, p < .01]. In exploratory analyses, emotion regulation served as a mediator for the link between CM and PPD as well as for the link between CM and parenting stress.

Limitations: The study design is cross-sectional and based on self-report questionnaires. Despite our attempts, only few fathers participated in the study, resulting in an underpowered sample for the regression analyses.

Conclusions: The study confirmed the assumption that CM experiences represent a risk factor for the development of maternal PPD. Emotion regulation might provide a pivotal target for interventions with parents at-risk.

Objective and quantitative neuroimaging biomarkers are crucial for early diagnosis of major depressive disorder (MDD). However, previous studies using machine learning (ML) to distinguish MDD have often used small sample sizes and overlooked MDD's neural connectome and mechanism. To address these gaps, we applied Dynamic Graph Convolutional Nets (DGCNs) to a large multi-site dataset of 2317 resting state functional MRI (RS-fMRI) scans from 1081 MDD patients and 1236 healthy controls from 16 Rest-meta-MDD consortium sites. Our DGCN model combined with the personal whole brain functional connectivity (FC) network achieved an accuracy of 82.5 % (95 % CI:81.6-83.4 %, AUC:0.869), outperforming other universal ML classifiers. The most prominent domains for classification were mainly in the default mode network, fronto-parietal and cingulo-opercular network. Our study supports the stability and efficacy of using DGCN to characterize MDD and demonstrates its potential in enhancing neurobiological comprehension of MDD by detecting clinically related disorders in FC network topologies.

Background: Human's response inhibition could be classified as proactive (top-down) and reactive (bottom-up) inhibition process. Although trait anxiety has been proposed to alter an individual's response inhibition, whether and how it modulates the proactive and reactive response inhibition processes in different emotional contexts remains unclear.

Methods: We combined an adapted emotional Stop-Signal Task with high-temporal resolution electrophysiological recordings to compare the results of high and low trait anxiety participants.

Results: At the behavioral level, no significant differences were found between high and low trait anxiety individuals in either proactive or reactive inhibition. However, the event-related potential analysis suggested that although trait anxiety might affect early object recognition, indicated by a greater P2 amplitude, high trait anxiety individuals may exhibit intact proactive inhibition but impaired reactive inhibition, signified by a delayed P3 peak latency. In addition, behavioral and event-related potential analyses revealed that negative emotional context impaired reactive inhibition independent of the effect of trait anxiety.

Conclusions: Trait anxiety impairs reactive inhibition but not proactive inhibition independently of neutral or negative emotional contexts.

Background: The Mendelian randomization approach has emerged as a powerful tool, leveraging genetic variations as natural random experiments to minimize confounding and infer causality with unique advantages. Previous research has highlighted the crucial roles of gut microbiotas and inflammatory factors in mental-behavioral disorders, albeit to varying degrees. However, the precise causal relationship between gut microbiotas and mental-behavioral disorders remains elusive, and the potential role of inflammatory factors as mediators in this process is unclear.

Methods: To investigate the associations between gut microbiotas, inflammatory factors, and mental-behavioral disorders, we pooled data from large-scale genome-wide association studies (GWAS). Our study screened 27 diseases, encompassing nine subtypes of mental-behavioral disorders: neurodevelopmental disorders, eating disorders, sleep disorders, schizophrenia spectrum disorders, stress- and trauma-related disorders, mood and affective disorders, cognitive and executive function disorders, personality and somatization disorders, and addiction disorders. Mendelian randomization(MR) was employed to assess causal relationships between gut microbiotas, inflammatory factors, and these mental-behavioral disorders, with inverse variance weighting (IVW) as the primary statistical method. Furthermore, we explored whether inflammatory factors mediate the relationship between gut microbiotas and mental-behavioral disorders.

Results: Having investigated the intricate interplay among gut microbiota, inflammatory factors, and mental-behavioral disorders, we have identified nine pivotal inflammatory factors that intricately regulate the progression of eight distinct disease subtypes. Noteworthy among these findings, levels of CC motif chemokine ligand 28 (CCL28) and CC motif chemokine ligand 25 (CCL25) are associated with the progression of attention-deficit/hyperactivity disorder (ADHD), interleukin-18 (IL-18) levels are linked to anorexia, IL-12β levels are related to schizophrenia (SZ) progression, IL-8 levels are associated with manic episodes, and IL-10 and monocyte chemoattractant protein-2 (MCP-2) levels are closely related to enduring personality changes(EPC). Additionally, fibroblast growth factor 19 (FGF19) levels are associated with cognitive disorders, while C-X-C motif chemokine ligand 1 (CXCL1) levels are related to executive functioning.

Conclusion: Gut microbiotas and mental-behavioral disorders have causal relationships, with inflammatory factors mediating the pathway from gut microbiotas to these disorders.

Background: The objective of the study was to investigate the prevalence of suicidal ideation disclosure over the past two decades in nationally representative samples of the general population, and to identify factors associated with disclosure.

Methods: Data were drawn from consecutive nationally representative cross-sectional Health Barometer surveys. The 2000, 2005, 2010, 2014, 2017, and 2021 waves were pooled to examine disclosure among those who reported 12-month suicidal ideation. Logistic regressions were performed to identify factors associated with the odds of disclosure.

Results: Across all waves (n = 124,124), 6014 of adults (4.7 %) reported 12-month suicidal ideation, and among them, 49.7 % talked to someone about it. Disclosure was 39 % in 2000, 44.6 % in 2005, 49.9 % in 2010, 52.8 % in 2014, 47.2 % in 2017, and 64.8 % in 2021. Female gender, a prior suicide attempt, higher education, inactive status, and younger age were associated with significantly greater odds of disclosure. Each survey wave was also associated with a greater likelihood of disclosure when compared to 2000, 1.31 (95 % CI, 1.08-1.59) in 2005, 1.69 (95 % CI, 1.38-2.07) in 2010, 1.89 (95 % CI, 1.52-2.34) in 2014, 1.47 (95 % CI, 1.21-1.79) in 2017, and 2.99 (95 % CI, 2.43-3.68) in 2021.

Limitations: Cross-sectional surveys.

Conclusions: In the general population of France, adults with suicidal ideation were increasingly more likely to disclose their ideation to someone in recent years. Factors associated with odds of disclosure should inform national suicide prevention strategies to identify subgroups who remain less likely than others to disclose their ideation.

Backgrounds: Evidence from animal and population studies has consistently revealed that microRNA 218 (MIR218) is involved in susceptibility to depression and cognitive functions. Nevertheless, few studies have evaluated the association between MIR218 and clinical features in patients with depressed bipolar disorder (BD).

Methods: A total of 66 patients with depressed BD and 49 healthy controls (HCs) were recruited for this study. MIR218 polygenic risk score (PRS) was used to assess the addictive effects of the MIR218 regulated genes. We compared the MIR218 PRS between patients with depressed BD and HCs to investigate whether it can be used to predict the risk of BD, and further explored the association between MIR218 PRS and cognitive performance as well as neurochemical metabolites among depressed BD.

Results: We found that there was a significant difference in MIR218 PRS between patients with depressed BD and HCs. The correlation analysis indicated that MIR218 PRS was negative associated with the number of disease onset (r = -0.311, P = 0.033) and choline (Cho)/creatine (Cr) in right thalamus (r = -0.285, P = 0.021). Additionally, as supported by previous findings, patients with lower MIR218 PRS presented more domains of impaired cognitive function than those with higher scores.

Conclusion: These findings suggested MIR218 PRS might be useful in differentiating patients with depressed BD from HCs. Moreover, depressed BD with lower MIR218 PRS showed more pronounced cognitive impairment than those with higher scores, which may be associated with disease recurrence and Cho metabolism in right thalamus.

Background: Military personnel face unique stressors to their mental health.

Objectives: To estimate the prevalence of serious psychological distress among active-duty U.S. service members who utilized mental health services, and to identify related risk factors.

Methods: We applied a cross-sectional secondary data analysis design utilizing the 2018 DoD Health Related Behaviors Survey. The primary outcome was serious psychological distress during the past 12 months as measured by the Kessler 6-item Psychological Distress Scale.

Results: The weighted prevalence of past-year serious psychological distress among service members utilizing mental health services was 39.1 % (95 % CI: 36.7-41.6 %). Significantly increased odds of serious psychological distress were seen among those who were separated, widowed, or divorced, and those in the Army, Navy, or Marine Corps. Decreased odds were seen for those in higher paygrades and those with at least a bachelor's degree (p < 0.05 for all). Smoking, binge drinking, illicit drug use, and sleeping ≤6 h per night were associated with serious psychological distress (p < 0.05 for all).

Conclusion: Among active-duty service members who utilized mental health services, 39.1 % reported serious psychological distress over the past year. Being separated, widowed, or divorced and having a lower education level were associated with serious psychological distress. Sex, race/ethnicity, and lesbian/gay/bisexual identity were not found to be correlated with the outcome. Additional research is needed to further explore these correlations to enhance military readiness.

Background: Differentiating between patients with bipolar disorder (BD) and major depressive disorder (MDD) is clinically challenging. This study aimed to explore the potential of radiomic textural features for discriminating BD and MDD.

Methods: A total 253 subjects (114 patients with BD, 139 patients with MDD) with T1-weighted MRI data were recruited. Radiomics features and gray matter volume (GMV) features were extracted from each brain region. A novel high-level MSN_II feature method based on radiomic features was proposed. And a total of 21 MSN features (5 MSN_I and 16 MSN_II) based on different combinations of the 5 types of radiomic textural feature were calculated. Classification models were constructed using various combinations of MSNs or GMV, and their performance and stability was evaluated through 2000 repeated experiments.

Results: The model built with combined features (GMV and GMV + MSN_II_GLCM_GLSZM_NGTDM) showed the best classification performance (AUC = 0.896±0.058, ACC = 0.831±0.064) in the validation cohort. After MANOVA analysis and FDR correlation, the MSN_II_GLCM_GLSZM_NGTDM values in 4 regions (right rectus gyrus, right temporal pole: middle temporal gyrus, Vermis3 and Vermis10) showed significant difference between BD and MDD.

Limitation: The main limitation of this study is that the data is derived from a single center without an external independent test set.

Conclusions: Incorporating the high-level MSN_II based on radiomics features can improve the classification performance compared to models solely relying on GMV features alone. This result implied the potential application of the proposed high level MSN method and radiomics textural features on the MDD and BD clinical studies.

Background: Children's loneliness has become an increasingly pervasive issue of public health due to the vulnerability of school-aged children. This study aims to identify latent classes of school-age children based on their exhibited symptoms of loneliness and explore the influencing factors.

Methods: A cross-sectional study was conducted from March to June 2023 in Shantou, China. Demographic characteristics, Abbreviated Symptom Questionnaire (ASQ), Children's Loneliness Scale (CLS), Perceived social support Scale (PSSS), and Children's Hope Scale (CHS) were collected by questionnaires. Latent class analysis (LCA) was performed based on loneliness symptoms among school-age children, with class characteristics and influencing factors explored through chi-square tests, analysis of variance, lasso regression, and multinomial logistic regression analyses.

Results: A total of 2514 school-age children were enrolled. Four diverse latent classes were identified, namely, the low loneliness group, the borderline loneliness group, the moderate loneliness group, and the high loneliness group, with 37.0 %, 40.4 %, 10.3 % and 12.3 % in each class, respectively. Compared with the low loneliness group, the factors influencing loneliness symptoms in other groups were grade, academic performance, father's education level, experience of being bullied, experience of being physical attacked, homework help from parents, one-child status, number of friends, relationship with friends, feeling respect from parents, perceived social support, as well as hope (all P < 0.05).

Limitations: The study's cross-sectional design, limited sample and area, and self-reporting method may affect the findings' reliability and generalizability.

Conclusions: LCA can categorize different school-age children according to their loneliness symptoms, offering a new perspective of addressing loneliness issues.

Background: Depression is one of the most widespread diseases worldwide, with the highest rates of disability. Considering its chronic course, over the years several treatment options have been developed and validated, however still with high relapse rates. Therefore, in recent years, the so-called third wave psychotherapies have been developed for the treatment of psychiatric disorders. Among these, the Metacognitive therapy (MCT) has proven to be effective in treating depression. The aim of this review is to evaluate the efficacy of MCT as monotherapy or adjunctive treatment in reducing depressive symptoms in patients suffering from major depression or dysthymia.

Methods: From bibliographic research in PubMed until December 2023, we retrieved 12 original studies meeting our research criteria.

Results: The total sample of patients undergoing metacognitive therapy (MCT) included 376 individuals, while the control groups comprised 300 subjects, with a gender ratio of the participants of 0.48 %. The results show that metacognitive therapy is an effective approach in reducing depressive symptoms in patients with a diagnosis of depression or dysthymia when used as add-on therapy, with an efficacy comparable to CBT and superior to pharmacotherapy and as monotherapy, with an efficacy comparable to therapy with antidepressants compared to the control group and compared to cognitive-behavioral therapy with higher rates of reduction of depressive symptoms after treatment) and at six months. Preliminary data also indicate its efficacy in terms of reduction of depressive symptoms in elderly people, suggesting its possible use in this population.

Limitations: The methodological heterogeneity in terms of treatment protocols of MCT and treatment control as well as the clinical heterogeneity of the sample employed may have limited the generalizability of the results.

Conclusions: The results suggest that the use of MCT, both as monotherapy and as an add-on treatment, is a valid therapeutic option for major depression, even at the later stages. However, further studies are needed for deeper our comprehension of the efficacy of MCT in depression.